Abstract
We propose methods to automatically assess the conservation status of a habitat. Habitat monitoring is usually performed by botanists and other specialists in their field work, searching for the presence or lack of typical plant species (Evans D, Arvela M (2011) Assessment and reporting under Article 17 of the Habitats Directive. Explanatory Notes & Guidelines for the period 2007–2012. European Commission, Brussels.) and other elements (such as vegetation cover) that might indicate the degradation of a habitat. We present preliminary work that makes use of a robotic platform employed to help botanists in their tasks. Three methods are proposed. First a color segmentation method, to detect the amount of green in a given area, a detection method to automatically detect the presence of a given plant, and finally a classification method used to identify a plant in a single image.
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Notes
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Grant agreement No. 101016970, European Union’s Horizon 2020 Research and Innovation Programme - ICT-47-2020.
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Douglas Evans and Marita Arvela. Assessment and reporting under article 17 of the habitats directive. explanatory notes & guidelines for the period 2007–2012. European Commission, Brussels, 2011.
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Habitats Directive. Council directive 92/43/EEC of 21 may 1992 on the conservation of natural habitats and of wild fauna and flora. Official Journal of the European Union, 206:7–50, 1992.
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References
Bonari, G.EA.: Shedding light on typical species: implications for habitat monitoringl. Plant Sociol. 58(1), 157–166 (2021)
Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)
Chollet, F.: Xception: deep learning with depthwise separable convolutions. corr abs/1610.02357 (2016). arXiv preprint arXiv:1610.02357 (2016)
Enkvetchakul, P., Surinta, O.: Effective data augmentation and training techniques for improving deep learning in plant leaf disease recognition. Appl. Sci. Eng. Progr. (2021)
Gao, Z., Li, M., Li, W., Yan, Q.: Classification of flowers under complex background using inception-v3 network. In: Proceedings of the 2020 4th International Conference on Deep Learning Technologies (ICDLT), pp. 113–117 (2020)
Garcia-Lamont, F., Cervantes, J., López, A., Rodriguez, L.: Segmentation of images by color features: a survey. Neurocomputing 292, 1–27 (2018)
Garcin, C., et al.: Pl@ ntnet-300k: a plant image dataset with high label ambiguity and a long-tailed distribution. In: NeurIPS 2021–35th Conference on Neural Information Processing Systems (2021)
Gigante, D., et al.: A methodological protocol for annex in habitat monitoring: the contribution of vegetation science. Plant Sociol. 53, 77–87 (2016)
Hamuda, E., Glavin, M., Jones, E.: A survey of image processing techniques for plant extraction and segmentation in the field. Comput. Electron. Agric. 125, 184–199 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arxiv 2015. arXiv preprint arXiv:1512.03385 (2015)
Jiang, P.T., Zhang, C.B., Hou, Q., Cheng, M.M., Wei, Y.: LayerCam: exploring hierarchical class activation maps for localization. IEEE Trans. Image Process. 30, 5875–5888 (2021)
Jiang, Y., Li, C., Xu, R., Sun, S., Robertson, J.S., Paterson, A.H.: Deepflower: a deep learning-based approach to characterize flowering patterns of cotton plants in the field. Plant Methods 16(1), 1–17 (2020)
Jongman, R.: Biodiversity observation from local to global. Ecol. Indicators 33, 1–4 (2013)
Kaya, A., Keceli, A.S., Catal, C., Yalic, H.Y., Temucin, H., Tekinerdogan, B.: Analysis of transfer learning for deep neural network based plant classification models. Comput. Electron. Agric. 158, 20–29 (2019)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)
Lee, S.H., Chan, C.S., Wilkin, P., Remagnino, P.: Deep-plant: plant identification with convolutional neural networks. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 452–456. IEEE (2015)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Minervini, M., Fischbach, A., Scharr, H., Tsaftaris, S.A.: Finely-grained annotated datasets for image-based plant phenotyping. Pattern Recogn. Lett. 81, 80–89 (2016)
Nguyen, T.T.N., Le, V., Le, T., Hai, V., Pantuwong, N., Yagi, Y.: Flower species identification using deep convolutional neural networks. In: AUN/SEED-Net Regional Conference for Computer and Information Engineering (2016)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Remagnino, P., Mayo, S., Wilkin, P., Cope, J., Kirkup, D.: Computational Botany. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-53745-9
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Selvaraju, R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-Cam: visual explanations from deep networks via gradient-based localization. arXiv preprint arXiv:1610.02391 (2016)
Szegedy, C., et al.: Going deeper with convolutions. arXiv preprint arXiv:1409.4842, p. 1409 (2014)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. arXiv preprint arXiv:1512.00567 (2015)
Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., He, Q.: A comprehensive survey on transfer learning. Proc. IEEE 109(1), 43–76 (2020)
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This research is supported by Grant Agreement No. 10101697, under the European Union’s Horizon2020 Research and Innovation Programme.
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Manh, X.H. et al. (2023). Towards the Computational Assessment of the Conservation Status of a Habitat. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13806. Springer, Cham. https://doi.org/10.1007/978-3-031-25075-0_51
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